在下面的最小示例中, foo 实例不会被释放,尽管超出范围并且没有引用者(除了 lru_cache)。 from functools import lru_cache class BigClass: pass class Foo: def __init__(self): self.big = BigClass() @lru_cache(maxsize=16) def cached_method(self, x): return x + 5 def fun(): foo = ...
from cachetools import LRUCache,RRCache , cachedmethod class Model: def __init__(self, cachesize): self.cache = LRUCache(maxsize=cachesize) @cachedmethod(attrgetter('cache')) def get_double_num(self, num): """ return 2* num""" print(f'get_double_num({num}) is running') time.sl...
4.1 使用functools.lru_cache Python标准库中的functools.lru_cache是最简便的缓存装饰器实现方式 ,它实现了最近最少使用(Least Recently Used)缓存策略。 from functools import lru_cache @lru_cache(maxsize=128) def fibonacci(n): if n < 2: return n return fibonacci(n-1) + fibonacci(n-2) print(fi...
'Cache','LFUCache','LRUCache','RRCache','TTLCache', ‘cached’, ‘cachedmethod’ 这两个分别用来修饰 函数和成员方法的. 2 来看一个 缓存函数 # 缓存 函数的值fromcachetoolsimportcached@cached(cache={})deffib(n):print((f'fib({n}) is running.'))returnnifn <2elsefib(n -1) + fib(n ...
class OldLibraryAPI: def legacy_method(self): return "This comes from an old library." # 适配器类,提供新接口 class NewLibraryAdapter: def __init__(self): self.old_api = OldLibraryAPI() def modern_method(self): return self.old_api.legacy_method() + " (adapted for new system)" ...
1importtime2importfunctools3importcollections45deflru_cache(maxsize = 255, timeout =None):6"""lru_cache(maxsize = 255, timeout = None) --> returns a decorator which returns an instance (a descriptor).78Purpose - This decorator factory will wrap a function / instance method and will suppl...
lru_cache(maxsize=4) ... def fibonacci(num): ... if num < 2: ... value = num ... else: ... value = fibonacci(num - 1) + fibonacci(num - 2) ... print(f"Calculated fibonacci({num}) = {value}") ... return value ... The maxsize parameter specifies how many ...
class EmailSender(Protocol): def __call__(self, to_email: str, subject: str, body: str) -> None: ... @lru_cache def bmi(weight: float, height: float) -> float: return weight / (height**2) def bmi_category(bmi_value: float) -> str: if bmi_value < 18.5: return "Underweigh...
@functools.lru_cache(maxsize=128, typed=False) New in version 3.2. Changed in version 3.3: Added the typed option. 这个装饰器实现了备忘的功能,是一项优化技术,把耗时的函数的结果保存起来,避免传入相同的参数时重复计算。lru 是(least recently used)的缩写,即最近最少使用原则。表明缓存不会无限制增长...
from functools import lru_cache @lru_cache(maxsize=128) def fibonacci(n): if n <= 1: return n else: return (fibonacci(n-1) + fibonacci(n-2)) print(fibonacci(30)) # 只计算一次 注意:lru_cache是一个内置的缓存实现,非常适合用于耗时的计算任务。